Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory180.6 KiB
Average record size in memory168.1 B

Variable types

Numeric17
Categorical4

Alerts

anxiety_level has 29 (2.6%) zeros Zeros
self_esteem has 24 (2.2%) zeros Zeros
depression has 44 (4.0%) zeros Zeros
headache has 32 (2.9%) zeros Zeros
sleep_quality has 34 (3.1%) zeros Zeros
breathing_problem has 43 (3.9%) zeros Zeros
noise_level has 36 (3.3%) zeros Zeros
living_conditions has 29 (2.6%) zeros Zeros
safety has 33 (3.0%) zeros Zeros
basic_needs has 40 (3.6%) zeros Zeros
academic_performance has 28 (2.5%) zeros Zeros
study_load has 37 (3.4%) zeros Zeros
teacher_student_relationship has 42 (3.8%) zeros Zeros
future_career_concerns has 30 (2.7%) zeros Zeros
peer_pressure has 37 (3.4%) zeros Zeros
extracurricular_activities has 34 (3.1%) zeros Zeros
bullying has 39 (3.5%) zeros Zeros

Reproduction

Analysis started2025-09-02 10:01:40.549760
Analysis finished2025-09-02 10:02:02.321291
Duration21.77 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

anxiety_level
Real number (ℝ)

Zeros 

Distinct22
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.063636
Minimum0
Maximum21
Zeros29
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:02.374621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q316
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1175582
Coefficient of variation (CV)0.55294281
Kurtosis-1.0941325
Mean11.063636
Median Absolute Deviation (MAD)5
Skewness-0.082611515
Sum12170
Variance37.424518
MonotonicityNot monotonic
2025-09-02T15:32:02.443470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13 67
 
6.1%
10 63
 
5.7%
19 61
 
5.5%
21 61
 
5.5%
9 60
 
5.5%
12 56
 
5.1%
18 55
 
5.0%
11 54
 
4.9%
6 52
 
4.7%
7 51
 
4.6%
Other values (12) 520
47.3%
ValueCountFrequency (%)
0 29
2.6%
1 46
4.2%
2 46
4.2%
3 48
4.4%
4 33
3.0%
5 40
3.6%
6 52
4.7%
7 51
4.6%
8 44
4.0%
9 60
5.5%
ValueCountFrequency (%)
21 61
5.5%
20 40
3.6%
19 61
5.5%
18 55
5.0%
17 47
4.3%
16 45
4.1%
15 51
4.6%
14 51
4.6%
13 67
6.1%
12 56
5.1%

self_esteem
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.777273
Minimum0
Maximum30
Zeros24
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:02.515212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median19
Q326
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.9445991
Coefficient of variation (CV)0.50314799
Kurtosis-1.0618295
Mean17.777273
Median Absolute Deviation (MAD)7
Skewness-0.39653941
Sum19555
Variance80.005852
MonotonicityNot monotonic
2025-09-02T15:32:02.587982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25 83
 
7.5%
30 60
 
5.5%
27 59
 
5.4%
26 58
 
5.3%
28 53
 
4.8%
29 52
 
4.7%
15 50
 
4.5%
23 45
 
4.1%
17 40
 
3.6%
21 38
 
3.5%
Other values (21) 562
51.1%
ValueCountFrequency (%)
0 24
2.2%
1 28
2.5%
2 21
1.9%
3 25
2.3%
4 19
1.7%
5 19
1.7%
6 31
2.8%
7 28
2.5%
8 26
2.4%
9 25
2.3%
ValueCountFrequency (%)
30 60
5.5%
29 52
4.7%
28 53
4.8%
27 59
5.4%
26 58
5.3%
25 83
7.5%
24 30
 
2.7%
23 45
4.1%
22 27
 
2.5%
21 38
3.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
0
558 
1
542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

Length

2025-09-02T15:32:02.660099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:32:02.707431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

Most occurring characters

ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 558
50.7%
1 542
49.3%

depression
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.555455
Minimum0
Maximum27
Zeros44
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:02.760744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q319
95-th percentile26
Maximum27
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.7270084
Coefficient of variation (CV)0.6154304
Kurtosis-0.98884476
Mean12.555455
Median Absolute Deviation (MAD)6
Skewness0.21663777
Sum13811
Variance59.706658
MonotonicityNot monotonic
2025-09-02T15:32:02.834745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
10 66
 
6.0%
12 63
 
5.7%
13 63
 
5.7%
14 57
 
5.2%
8 49
 
4.5%
3 47
 
4.3%
9 46
 
4.2%
11 46
 
4.2%
0 44
 
4.0%
5 43
 
3.9%
Other values (18) 576
52.4%
ValueCountFrequency (%)
0 44
4.0%
1 36
3.3%
2 36
3.3%
3 47
4.3%
4 35
3.2%
5 43
3.9%
6 36
3.3%
7 42
3.8%
8 49
4.5%
9 46
4.2%
ValueCountFrequency (%)
27 36
3.3%
26 33
3.0%
25 27
2.5%
24 34
3.1%
23 27
2.5%
22 36
3.3%
21 28
2.5%
20 33
3.0%
19 29
2.6%
18 41
3.7%

headache
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5081818
Minimum0
Maximum5
Zeros32
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:02.897771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.409356
Coefficient of variation (CV)0.56190343
Kurtosis-0.99659381
Mean2.5081818
Median Absolute Deviation (MAD)1
Skewness0.27049441
Sum2759
Variance1.9862842
MonotonicityNot monotonic
2025-09-02T15:32:02.954519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 331
30.1%
3 287
26.1%
2 181
16.5%
4 140
12.7%
5 129
 
11.7%
0 32
 
2.9%
ValueCountFrequency (%)
0 32
 
2.9%
1 331
30.1%
2 181
16.5%
3 287
26.1%
4 140
12.7%
5 129
 
11.7%
ValueCountFrequency (%)
5 129
 
11.7%
4 140
12.7%
3 287
26.1%
2 181
16.5%
1 331
30.1%
0 32
 
2.9%

blood_pressure
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
3
500 
1
300 
2
300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

Length

2025-09-02T15:32:03.020830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:32:03.075169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

Most occurring characters

ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 500
45.5%
1 300
27.3%
2 300
27.3%

sleep_quality
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.66
Minimum0
Maximum5
Zeros34
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.121891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2.5
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5483826
Coefficient of variation (CV)0.58209872
Kurtosis-1.3220488
Mean2.66
Median Absolute Deviation (MAD)1.5
Skewness0.1779749
Sum2926
Variance2.3974886
MonotonicityNot monotonic
2025-09-02T15:32:03.179868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 328
29.8%
5 192
17.5%
2 188
17.1%
4 188
17.1%
3 170
15.5%
0 34
 
3.1%
ValueCountFrequency (%)
0 34
 
3.1%
1 328
29.8%
2 188
17.1%
3 170
15.5%
4 188
17.1%
5 192
17.5%
ValueCountFrequency (%)
5 192
17.5%
4 188
17.1%
3 170
15.5%
2 188
17.1%
1 328
29.8%
0 34
 
3.1%

breathing_problem
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7536364
Minimum0
Maximum5
Zeros43
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.233272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4007133
Coefficient of variation (CV)0.50867765
Kurtosis-1.0600442
Mean2.7536364
Median Absolute Deviation (MAD)1
Skewness-0.001624539
Sum3029
Variance1.9619977
MonotonicityNot monotonic
2025-09-02T15:32:03.291297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 325
29.5%
4 287
26.1%
1 179
16.3%
3 139
12.6%
5 127
 
11.5%
0 43
 
3.9%
ValueCountFrequency (%)
0 43
 
3.9%
1 179
16.3%
2 325
29.5%
3 139
12.6%
4 287
26.1%
5 127
 
11.5%
ValueCountFrequency (%)
5 127
 
11.5%
4 287
26.1%
3 139
12.6%
2 325
29.5%
1 179
16.3%
0 43
 
3.9%

noise_level
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6490909
Minimum0
Maximum5
Zeros36
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.345225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3281268
Coefficient of variation (CV)0.50135193
Kurtosis-0.69576393
Mean2.6490909
Median Absolute Deviation (MAD)1
Skewness0.20712185
Sum2914
Variance1.7639209
MonotonicityNot monotonic
2025-09-02T15:32:03.401504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 313
28.5%
3 289
26.3%
1 188
17.1%
4 137
12.5%
5 137
12.5%
0 36
 
3.3%
ValueCountFrequency (%)
0 36
 
3.3%
1 188
17.1%
2 313
28.5%
3 289
26.3%
4 137
12.5%
5 137
12.5%
ValueCountFrequency (%)
5 137
12.5%
4 137
12.5%
3 289
26.3%
2 313
28.5%
1 188
17.1%
0 36
 
3.3%

living_conditions
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5181818
Minimum0
Maximum5
Zeros29
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.457312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1192079
Coefficient of variation (CV)0.4444508
Kurtosis-0.49691718
Mean2.5181818
Median Absolute Deviation (MAD)1
Skewness0.018794045
Sum2770
Variance1.2526264
MonotonicityNot monotonic
2025-09-02T15:32:03.515542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 346
31.5%
3 327
29.7%
4 189
17.2%
1 176
16.0%
5 33
 
3.0%
0 29
 
2.6%
ValueCountFrequency (%)
0 29
 
2.6%
1 176
16.0%
2 346
31.5%
3 327
29.7%
4 189
17.2%
5 33
 
3.0%
ValueCountFrequency (%)
5 33
 
3.0%
4 189
17.2%
3 327
29.7%
2 346
31.5%
1 176
16.0%
0 29
 
2.6%

safety
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7372727
Minimum0
Maximum5
Zeros33
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.572253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4061712
Coefficient of variation (CV)0.51371249
Kurtosis-0.9998319
Mean2.7372727
Median Absolute Deviation (MAD)1
Skewness0.1880967
Sum3011
Variance1.9773174
MonotonicityNot monotonic
2025-09-02T15:32:03.630773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 348
31.6%
4 184
16.7%
1 184
16.7%
3 180
16.4%
5 171
15.5%
0 33
 
3.0%
ValueCountFrequency (%)
0 33
 
3.0%
1 184
16.7%
2 348
31.6%
3 180
16.4%
4 184
16.7%
5 171
15.5%
ValueCountFrequency (%)
5 171
15.5%
4 184
16.7%
3 180
16.4%
2 348
31.6%
1 184
16.7%
0 33
 
3.0%

basic_needs
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7727273
Minimum0
Maximum5
Zeros40
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.685670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4337609
Coefficient of variation (CV)0.51709411
Kurtosis-1.0085225
Mean2.7727273
Median Absolute Deviation (MAD)1
Skewness0.13585902
Sum3050
Variance2.0556704
MonotonicityNot monotonic
2025-09-02T15:32:03.744145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 335
30.5%
3 190
17.3%
5 189
17.2%
1 173
15.7%
4 173
15.7%
0 40
 
3.6%
ValueCountFrequency (%)
0 40
 
3.6%
1 173
15.7%
2 335
30.5%
3 190
17.3%
4 173
15.7%
5 189
17.2%
ValueCountFrequency (%)
5 189
17.2%
4 173
15.7%
3 190
17.3%
2 335
30.5%
1 173
15.7%
0 40
 
3.6%

academic_performance
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7727273
Minimum0
Maximum5
Zeros28
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.798664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4145937
Coefficient of variation (CV)0.51018134
Kurtosis-1.063433
Mean2.7727273
Median Absolute Deviation (MAD)1
Skewness0.18431609
Sum3050
Variance2.0010754
MonotonicityNot monotonic
2025-09-02T15:32:03.857442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 348
31.6%
4 188
17.1%
1 185
16.8%
5 182
16.5%
3 169
15.4%
0 28
 
2.5%
ValueCountFrequency (%)
0 28
 
2.5%
1 185
16.8%
2 348
31.6%
3 169
15.4%
4 188
17.1%
5 182
16.5%
ValueCountFrequency (%)
5 182
16.5%
4 188
17.1%
3 169
15.4%
2 348
31.6%
1 185
16.8%
0 28
 
2.5%

study_load
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6218182
Minimum0
Maximum5
Zeros37
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:03.912554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315781
Coefficient of variation (CV)0.50185822
Kurtosis-0.66083504
Mean2.6218182
Median Absolute Deviation (MAD)1
Skewness0.22896374
Sum2884
Variance1.7312797
MonotonicityNot monotonic
2025-09-02T15:32:03.969604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 333
30.3%
3 275
25.0%
1 185
16.8%
4 142
12.9%
5 128
 
11.6%
0 37
 
3.4%
ValueCountFrequency (%)
0 37
 
3.4%
1 185
16.8%
2 333
30.3%
3 275
25.0%
4 142
12.9%
5 128
 
11.6%
ValueCountFrequency (%)
5 128
 
11.6%
4 142
12.9%
3 275
25.0%
2 333
30.3%
1 185
16.8%
0 37
 
3.4%

teacher_student_relationship
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6481818
Minimum0
Maximum5
Zeros42
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:04.118919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3845785
Coefficient of variation (CV)0.52284118
Kurtosis-0.90290748
Mean2.6481818
Median Absolute Deviation (MAD)1
Skewness0.19801628
Sum2913
Variance1.9170577
MonotonicityNot monotonic
2025-09-02T15:32:04.175549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 353
32.1%
4 192
17.5%
1 190
17.3%
3 183
16.6%
5 140
 
12.7%
0 42
 
3.8%
ValueCountFrequency (%)
0 42
 
3.8%
1 190
17.3%
2 353
32.1%
3 183
16.6%
4 192
17.5%
5 140
 
12.7%
ValueCountFrequency (%)
5 140
 
12.7%
4 192
17.5%
3 183
16.6%
2 353
32.1%
1 190
17.3%
0 42
 
3.8%

future_career_concerns
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6490909
Minimum0
Maximum5
Zeros30
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:04.230819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5293754
Coefficient of variation (CV)0.57732085
Kurtosis-1.2972122
Mean2.6490909
Median Absolute Deviation (MAD)1
Skewness0.20427624
Sum2914
Variance2.3389892
MonotonicityNot monotonic
2025-09-02T15:32:04.286025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 327
29.7%
2 199
18.1%
5 186
16.9%
4 185
16.8%
3 173
15.7%
0 30
 
2.7%
ValueCountFrequency (%)
0 30
 
2.7%
1 327
29.7%
2 199
18.1%
3 173
15.7%
4 185
16.8%
5 186
16.9%
ValueCountFrequency (%)
5 186
16.9%
4 185
16.8%
3 173
15.7%
2 199
18.1%
1 327
29.7%
0 30
 
2.7%

social_support
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
3
458 
1
412 
2
142 
0
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

Length

2025-09-02T15:32:04.350802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:32:04.397401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

Most occurring characters

ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 458
41.6%
1 412
37.5%
2 142
 
12.9%
0 88
 
8.0%

peer_pressure
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7345455
Minimum0
Maximum5
Zeros37
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:04.446732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4252651
Coefficient of variation (CV)0.52120732
Kurtosis-1.0071759
Mean2.7345455
Median Absolute Deviation (MAD)1
Skewness0.19471919
Sum3008
Variance2.0313806
MonotonicityNot monotonic
2025-09-02T15:32:04.505106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 355
32.3%
5 181
16.5%
1 181
16.5%
4 174
15.8%
3 172
15.6%
0 37
 
3.4%
ValueCountFrequency (%)
0 37
 
3.4%
1 181
16.5%
2 355
32.3%
3 172
15.6%
4 174
15.8%
5 181
16.5%
ValueCountFrequency (%)
5 181
16.5%
4 174
15.8%
3 172
15.6%
2 355
32.3%
1 181
16.5%
0 37
 
3.4%

extracurricular_activities
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7672727
Minimum0
Maximum5
Zeros34
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:04.559285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2.5
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4175623
Coefficient of variation (CV)0.51225972
Kurtosis-1.0403076
Mean2.7672727
Median Absolute Deviation (MAD)1.5
Skewness0.13560359
Sum3044
Variance2.009483
MonotonicityNot monotonic
2025-09-02T15:32:04.616251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 332
30.2%
4 198
18.0%
1 184
16.7%
3 178
16.2%
5 174
15.8%
0 34
 
3.1%
ValueCountFrequency (%)
0 34
 
3.1%
1 184
16.7%
2 332
30.2%
3 178
16.2%
4 198
18.0%
5 174
15.8%
ValueCountFrequency (%)
5 174
15.8%
4 198
18.0%
3 178
16.2%
2 332
30.2%
1 184
16.7%
0 34
 
3.1%

bullying
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6172727
Minimum0
Maximum5
Zeros39
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-09-02T15:32:04.668984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.530958
Coefficient of variation (CV)0.58494401
Kurtosis-1.2744847
Mean2.6172727
Median Absolute Deviation (MAD)1
Skewness0.17534818
Sum2879
Variance2.3438324
MonotonicityNot monotonic
2025-09-02T15:32:04.727361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 335
30.5%
3 199
18.1%
4 187
17.0%
5 173
15.7%
2 167
15.2%
0 39
 
3.5%
ValueCountFrequency (%)
0 39
 
3.5%
1 335
30.5%
2 167
15.2%
3 199
18.1%
4 187
17.0%
5 173
15.7%
ValueCountFrequency (%)
5 173
15.7%
4 187
17.0%
3 199
18.1%
2 167
15.2%
1 335
30.5%
0 39
 
3.5%

stress_level
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
0
373 
2
369 
1
358 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Length

2025-09-02T15:32:04.792390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:32:04.836730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Most occurring characters

ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 373
33.9%
2 369
33.5%
1 358
32.5%

Interactions

2025-09-02T15:32:00.738483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:40.814287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:42.010657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:43.292310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:44.591819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:45.845044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:47.062817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:48.362092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:49.573380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:50.794314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:52.085251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:53.274758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:54.507565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:55.794938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:57.036458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:58.322778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:59.526291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:00.808546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:40.880739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:42.090849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:43.359193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:44.671705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:45.915936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:47.131066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:48.428602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:49.644828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:50.862357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:52.154434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:53.342163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:54.676413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:55.866116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:57.104900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:58.391015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:59.594719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:00.879991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:40.962318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:42.167232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:43.435002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:44.741347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:45.986351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:47.204890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:48.501141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:49.716540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:50.935443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:52.226063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:53.415881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:54.741277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:55.938080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:57.178530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:58.461330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:59.668243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:00.951501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:41.029478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:42.237317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:43.515396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:44.810500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:46.058030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:47.276349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:48.570626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:49.800766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:51.006245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:52.296000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:53.484619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:54.811848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:56.010692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:57.248589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:58.531439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:59.738317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:01.020922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:41.098805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:42.310892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:43.593724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:44.878036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:46.125365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:47.350090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:48.651044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:49.870182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:51.075065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:52.366747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:53.555881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:54.880185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:56.089354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:57.317070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:58.599483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:31:59.810200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:01.091025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-02T15:31:59.457876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T15:32:00.669823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-09-02T15:32:02.110450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-02T15:32:02.244576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditionssafetybasic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
0142001121242332323323321
115811553143122141514552
2121811421222232233223221
3161211543134222241414452
416280723513243431215051
5201312133143221252514452
64260612411444514132210
717312243153111132414452
8132211231243333332333221
96802743120522221515341
anxiety_levelself_esteemmental_health_historydepressionheadacheblood_pressuresleep_qualitybreathing_problemnoise_levelliving_conditionssafetybasic_needsacademic_performancestudy_loadteacher_student_relationshipfuture_career_concernssocial_supportpeer_pressureextracurricular_activitiesbullyingstress_level
109061102443110342032112010
1091191112543155112142415552
109213200921343332323233331
10931300412521355514131110
10947170043545101211101032
1095111701431322223222332331
10969120803000134011113432
10974260312522344514131210
109821011953143111251414442
109918611533033043343315142